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Real-time GNSS Precipitable Water Vapor Conversion with Sub-millimeter Accuracy based on Foundation Models

  • 1The Hong Kong Polytechnic University

  • 2Shanghai Astronomical Observatory

  • 3UCAS

  • 4Shanghai Key Laboratory of SNPT

  • 5GeoForschungsZentrum (GFZ)

  • 6Technische Universität Berlin

  • 7Shenzhen University

  • 8ETH Zurich

  • 9Shanghai AI Laboratory

Approach

Abstract

Global Navigation Satellite Systems (GNSSs) networks provide high-quality, high temporal resolution (5 min) tropospheric delay products at over 20,000 stations worldwide. However, the atmospheric vertical profile remains inaccessible at most sites due to the lack of in-situ meteorological sensors, which makes it challenging to convert high-accuracy tropospheric products to accurate precipitable water vapor (PWV). The continued development of empirical models has provided feasible solutions, but their inherent modeling errors continue to restrict the accuracy of real-time PWV conversion, and the current optimal method is still limited to the 2–3 mm level. We propose a real-time high-accuracy GNSS PWV conversion method based on artificial intelligence weather forecast foundation models. This innovative approach efficiently calculates integral zenith hydrostatic delay (ZHD) and integral weighted mean temperature (Tm) at any global location locally within seconds, bypassing inaccuracies inherent in empirical ZHD and Tm models, significantly reducing PWV errors. The method is evaluated on three representative foundation models, Pangu-Weather, GraphCast, and FengWu, showing root-mean-square errors of real-time GNSS PWV less than 1 mm. It offers a robust solution for real-time GNSS PWV conversion and has the potential to facilitate the assimilation of the increasing GNSS observations into global numerical weather prediction. In turn, employing more accurate atmospheric states that assimilate more GNSS observations as input can further enhance the method itself.

BibTeX

@article{ding2024forecasting, title={Real-time GNSS Precipitable Water Vapor Conversion with Sub-millimeter Accuracy based on Foundation Models}, author={Ding, Junsheng and Chen, Wu and Chen, Junping and Wang, Jungang and Zhang, Yize and Duojie, Weng and Tong, Liu and Mi, Xiaolong and Soja, Benedikt and Bai, Lei}, journal={Preprint}, year={2024}, publisher={****} }

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